When searching through a list, you might wonder why we sometimes prefer binary search over the simpler linear search. Here's a quick breakdown with a real-world example:

🔍 Linear Search:

• How it works: Checks each element one by one.

• Best use case: When the data is unsorted or small.

• Time complexity: O(n) — as the list grows, the search time increases proportionally.

⚡ Binary Search:

• How it works: Efficiently narrows down the search by repeatedly dividing the list in half.

• Best use case: When the data is sorted.

• Time complexity: O(log n) — even with large datasets, the search time grows slowly.

💡Example: Imagine you have an array with 2 million elements.

• Linear Search: In the worst case, you might have to check every element, making it take up to 2 million steps.

• Binary Search: Since binary search splits the list in half each time, it would only take about 21 steps (log₂(2,000,000) ≈ 21) to find the element, even in the worst case!

Happy coding! ✨

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